Overview

Dataset statistics

Number of variables36
Number of observations166345
Missing cells0
Missing cells (%)0.0%
Duplicate rows1881
Duplicate rows (%)1.1%
Total size in memory45.7 MiB
Average record size in memory288.0 B

Variable types

Categorical27
Numeric9

Alerts

Dataset has 1881 (1.1%) duplicate rowsDuplicates
HLTINSURE is highly overall correlated with AnyHealthcareHigh correlation
AnyHealthcare is highly overall correlated with HLTINSUREHigh correlation
CholCheck is highly imbalanced (71.7%)Imbalance
Stroke is highly imbalanced (70.4%)Imbalance
HvyAlcoholConsump is highly imbalanced (68.9%)Imbalance
AnyHealthcare is highly imbalanced (82.8%)Imbalance
NoDocbcCost is highly imbalanced (72.6%)Imbalance
CurrESmoke is highly imbalanced (86.2%)Imbalance
KIDNEYDIS is highly imbalanced (69.6%)Imbalance
RENT is highly imbalanced (54.4%)Imbalance
BLIND is highly imbalanced (69.3%)Imbalance
DECISION is highly imbalanced (56.9%)Imbalance
MentHlth has 115558 (69.5%) zerosZeros
PhysHlth has 109394 (65.8%) zerosZeros

Reproduction

Analysis started2024-06-17 23:59:31.412392
Analysis finished2024-06-18 00:00:17.560187
Duration46.15 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

Diabetes
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
0
135791 
1
30554 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters166345
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 135791
81.6%
1 30554
 
18.4%

Length

2024-06-17T20:00:17.631611image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-17T20:00:17.759084image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 135791
81.6%
1 30554
 
18.4%

Most occurring characters

ValueCountFrequency (%)
0 135791
81.6%
1 30554
 
18.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 166345
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 135791
81.6%
1 30554
 
18.4%

Most occurring scripts

ValueCountFrequency (%)
Common 166345
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 135791
81.6%
1 30554
 
18.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 166345
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 135791
81.6%
1 30554
 
18.4%

HighBP
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
1
86217 
0
80128 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters166345
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 86217
51.8%
0 80128
48.2%

Length

2024-06-17T20:00:17.862252image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-17T20:00:17.993691image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 86217
51.8%
0 80128
48.2%

Most occurring characters

ValueCountFrequency (%)
1 86217
51.8%
0 80128
48.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 166345
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 86217
51.8%
0 80128
48.2%

Most occurring scripts

ValueCountFrequency (%)
Common 166345
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 86217
51.8%
0 80128
48.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 166345
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 86217
51.8%
0 80128
48.2%

HighChol
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
0
84325 
1
82020 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters166345
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 84325
50.7%
1 82020
49.3%

Length

2024-06-17T20:00:18.095371image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-17T20:00:18.219867image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 84325
50.7%
1 82020
49.3%

Most occurring characters

ValueCountFrequency (%)
0 84325
50.7%
1 82020
49.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 166345
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 84325
50.7%
1 82020
49.3%

Most occurring scripts

ValueCountFrequency (%)
Common 166345
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 84325
50.7%
1 82020
49.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 166345
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 84325
50.7%
1 82020
49.3%

CholCheck
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
1
158146 
0
 
8199

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters166345
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 158146
95.1%
0 8199
 
4.9%

Length

2024-06-17T20:00:18.314603image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-17T20:00:18.427195image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 158146
95.1%
0 8199
 
4.9%

Most occurring characters

ValueCountFrequency (%)
1 158146
95.1%
0 8199
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 166345
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 158146
95.1%
0 8199
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
Common 166345
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 158146
95.1%
0 8199
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 166345
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 158146
95.1%
0 8199
 
4.9%

Smoker
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
0
92472 
1
73873 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters166345
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 92472
55.6%
1 73873
44.4%

Length

2024-06-17T20:00:18.515980image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-17T20:00:18.641963image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 92472
55.6%
1 73873
44.4%

Most occurring characters

ValueCountFrequency (%)
0 92472
55.6%
1 73873
44.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 166345
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 92472
55.6%
1 73873
44.4%

Most occurring scripts

ValueCountFrequency (%)
Common 166345
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 92472
55.6%
1 73873
44.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 166345
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 92472
55.6%
1 73873
44.4%

Stroke
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
0
157661 
1
 
8684

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters166345
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 157661
94.8%
1 8684
 
5.2%

Length

2024-06-17T20:00:18.734715image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-17T20:00:18.850283image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 157661
94.8%
1 8684
 
5.2%

Most occurring characters

ValueCountFrequency (%)
0 157661
94.8%
1 8684
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 166345
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 157661
94.8%
1 8684
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
Common 166345
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 157661
94.8%
1 8684
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 166345
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 157661
94.8%
1 8684
 
5.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
0
146996 
1
19349 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters166345
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 146996
88.4%
1 19349
 
11.6%

Length

2024-06-17T20:00:18.940554image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-17T20:00:19.057114image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 146996
88.4%
1 19349
 
11.6%

Most occurring characters

ValueCountFrequency (%)
0 146996
88.4%
1 19349
 
11.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 166345
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 146996
88.4%
1 19349
 
11.6%

Most occurring scripts

ValueCountFrequency (%)
Common 166345
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 146996
88.4%
1 19349
 
11.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 166345
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 146996
88.4%
1 19349
 
11.6%

PhysActivity
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
1
124216 
0
42129 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters166345
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 124216
74.7%
0 42129
 
25.3%

Length

2024-06-17T20:00:19.154330image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-17T20:00:19.267418image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 124216
74.7%
0 42129
 
25.3%

Most occurring characters

ValueCountFrequency (%)
1 124216
74.7%
0 42129
 
25.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 166345
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 124216
74.7%
0 42129
 
25.3%

Most occurring scripts

ValueCountFrequency (%)
Common 166345
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 124216
74.7%
0 42129
 
25.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 166345
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 124216
74.7%
0 42129
 
25.3%

Fruits
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
1
105464 
0
60881 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters166345
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 105464
63.4%
0 60881
36.6%

Length

2024-06-17T20:00:19.360169image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-17T20:00:19.477722image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 105464
63.4%
0 60881
36.6%

Most occurring characters

ValueCountFrequency (%)
1 105464
63.4%
0 60881
36.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 166345
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 105464
63.4%
0 60881
36.6%

Most occurring scripts

ValueCountFrequency (%)
Common 166345
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 105464
63.4%
0 60881
36.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 166345
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 105464
63.4%
0 60881
36.6%

Veggies
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
1
137375 
0
28970 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters166345
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 137375
82.6%
0 28970
 
17.4%

Length

2024-06-17T20:00:19.571465image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-17T20:00:19.694969image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 137375
82.6%
0 28970
 
17.4%

Most occurring characters

ValueCountFrequency (%)
1 137375
82.6%
0 28970
 
17.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 166345
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 137375
82.6%
0 28970
 
17.4%

Most occurring scripts

ValueCountFrequency (%)
Common 166345
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 137375
82.6%
0 28970
 
17.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 166345
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 137375
82.6%
0 28970
 
17.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
0
157029 
1
 
9316

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters166345
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 157029
94.4%
1 9316
 
5.6%

Length

2024-06-17T20:00:19.789706image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-17T20:00:19.902297image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 157029
94.4%
1 9316
 
5.6%

Most occurring characters

ValueCountFrequency (%)
0 157029
94.4%
1 9316
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 166345
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 157029
94.4%
1 9316
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Common 166345
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 157029
94.4%
1 9316
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 166345
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 157029
94.4%
1 9316
 
5.6%

AnyHealthcare
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
1
162087 
0
 
4258

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters166345
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 162087
97.4%
0 4258
 
2.6%

Length

2024-06-17T20:00:19.993066image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-17T20:00:20.110617image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 162087
97.4%
0 4258
 
2.6%

Most occurring characters

ValueCountFrequency (%)
1 162087
97.4%
0 4258
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 166345
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 162087
97.4%
0 4258
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Common 166345
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 162087
97.4%
0 4258
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 166345
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 162087
97.4%
0 4258
 
2.6%

NoDocbcCost
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
0
158490 
1
 
7855

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters166345
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 158490
95.3%
1 7855
 
4.7%

Length

2024-06-17T20:00:20.201385image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-17T20:00:20.313977image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 158490
95.3%
1 7855
 
4.7%

Most occurring characters

ValueCountFrequency (%)
0 158490
95.3%
1 7855
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 166345
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 158490
95.3%
1 7855
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
Common 166345
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 158490
95.3%
1 7855
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 166345
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 158490
95.3%
1 7855
 
4.7%

GenHlth
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5800174
Minimum0
Maximum5
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-06-17T20:00:20.394825image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0445379
Coefficient of variation (CV)0.4048569
Kurtosis-0.37937408
Mean2.5800174
Median Absolute Deviation (MAD)1
Skewness0.35836505
Sum429173
Variance1.0910594
MonotonicityNot monotonic
2024-06-17T20:00:20.487577image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 57956
34.8%
3 53306
32.0%
1 24895
15.0%
4 22487
 
13.5%
5 7700
 
4.6%
0 1
 
< 0.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 24895
15.0%
2 57956
34.8%
3 53306
32.0%
4 22487
 
13.5%
5 7700
 
4.6%
ValueCountFrequency (%)
5 7700
 
4.6%
4 22487
 
13.5%
3 53306
32.0%
2 57956
34.8%
1 24895
15.0%
0 1
 
< 0.1%

MentHlth
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2044726
Minimum0
Maximum30
Zeros115558
Zeros (%)69.5%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-06-17T20:00:20.601656image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile25
Maximum30
Range30
Interquartile range (IQR)2

Descriptive statistics

Standard deviation7.4103607
Coefficient of variation (CV)2.3125055
Kurtosis6.3320527
Mean3.2044726
Median Absolute Deviation (MAD)0
Skewness2.6978373
Sum533048
Variance54.913446
MonotonicityNot monotonic
2024-06-17T20:00:20.715736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 115558
69.5%
2 8384
 
5.0%
30 7762
 
4.7%
5 6011
 
3.6%
1 5033
 
3.0%
3 4834
 
2.9%
10 4435
 
2.7%
15 3891
 
2.3%
4 2409
 
1.4%
20 2243
 
1.3%
Other values (21) 5785
 
3.5%
ValueCountFrequency (%)
0 115558
69.5%
1 5033
 
3.0%
2 8384
 
5.0%
3 4834
 
2.9%
4 2409
 
1.4%
5 6011
 
3.6%
6 710
 
0.4%
7 1783
 
1.1%
8 492
 
0.3%
9 72
 
< 0.1%
ValueCountFrequency (%)
30 7762
4.7%
29 111
 
0.1%
28 227
 
0.1%
27 38
 
< 0.1%
26 34
 
< 0.1%
25 894
 
0.5%
24 31
 
< 0.1%
23 19
 
< 0.1%
22 39
 
< 0.1%
21 144
 
0.1%

PhysHlth
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2785175
Minimum0
Maximum30
Zeros109394
Zeros (%)65.8%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-06-17T20:00:20.840232image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile30
Maximum30
Range30
Interquartile range (IQR)3

Descriptive statistics

Standard deviation8.9211918
Coefficient of variation (CV)2.0851128
Kurtosis3.2546371
Mean4.2785175
Median Absolute Deviation (MAD)0
Skewness2.1688567
Sum711710
Variance79.587663
MonotonicityNot monotonic
2024-06-17T20:00:20.950840image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 109394
65.8%
30 13659
 
8.2%
2 8155
 
4.9%
1 6305
 
3.8%
3 4822
 
2.9%
5 4674
 
2.8%
10 3719
 
2.2%
15 3244
 
2.0%
4 2588
 
1.6%
7 2274
 
1.4%
Other values (21) 7511
 
4.5%
ValueCountFrequency (%)
0 109394
65.8%
1 6305
 
3.8%
2 8155
 
4.9%
3 4822
 
2.9%
4 2588
 
1.6%
5 4674
 
2.8%
6 731
 
0.4%
7 2274
 
1.4%
8 504
 
0.3%
9 93
 
0.1%
ValueCountFrequency (%)
30 13659
8.2%
29 117
 
0.1%
28 307
 
0.2%
27 64
 
< 0.1%
26 43
 
< 0.1%
25 894
 
0.5%
24 36
 
< 0.1%
23 24
 
< 0.1%
22 47
 
< 0.1%
21 399
 
0.2%

DiffWalk
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
0
132327 
1
34018 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters166345
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 132327
79.5%
1 34018
 
20.5%

Length

2024-06-17T20:00:21.068889image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-17T20:00:21.180488image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 132327
79.5%
1 34018
 
20.5%

Most occurring characters

ValueCountFrequency (%)
0 132327
79.5%
1 34018
 
20.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 166345
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 132327
79.5%
1 34018
 
20.5%

Most occurring scripts

ValueCountFrequency (%)
Common 166345
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 132327
79.5%
1 34018
 
20.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 166345
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 132327
79.5%
1 34018
 
20.5%

Sex
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
0
89472 
1
76873 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters166345
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 89472
53.8%
1 76873
46.2%

Length

2024-06-17T20:00:21.274728image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-17T20:00:21.387320image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 89472
53.8%
1 76873
46.2%

Most occurring characters

ValueCountFrequency (%)
0 89472
53.8%
1 76873
46.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 166345
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 89472
53.8%
1 76873
46.2%

Most occurring scripts

ValueCountFrequency (%)
Common 166345
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 89472
53.8%
1 76873
46.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 166345
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 89472
53.8%
1 76873
46.2%

Age
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
4
58336 
3
48396 
5
43317 
6
16296 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters166345
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row4
4th row5
5th row6

Common Values

ValueCountFrequency (%)
4 58336
35.1%
3 48396
29.1%
5 43317
26.0%
6 16296
 
9.8%

Length

2024-06-17T20:00:21.482552image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-17T20:00:21.605560image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
4 58336
35.1%
3 48396
29.1%
5 43317
26.0%
6 16296
 
9.8%

Most occurring characters

ValueCountFrequency (%)
4 58336
35.1%
3 48396
29.1%
5 43317
26.0%
6 16296
 
9.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 166345
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 58336
35.1%
3 48396
29.1%
5 43317
26.0%
6 16296
 
9.8%

Most occurring scripts

ValueCountFrequency (%)
Common 166345
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 58336
35.1%
3 48396
29.1%
5 43317
26.0%
6 16296
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 166345
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 58336
35.1%
3 48396
29.1%
5 43317
26.0%
6 16296
 
9.8%

Education
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0918332
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-06-17T20:00:21.701783image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q14
median5
Q36
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.95893649
Coefficient of variation (CV)0.18832834
Kurtosis0.15146103
Mean5.0918332
Median Absolute Deviation (MAD)1
Skewness-0.80923085
Sum847001
Variance0.9195592
MonotonicityNot monotonic
2024-06-17T20:00:21.894232image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
6 72300
43.5%
5 46918
28.2%
4 39715
23.9%
3 5066
 
3.0%
2 2207
 
1.3%
1 139
 
0.1%
ValueCountFrequency (%)
1 139
 
0.1%
2 2207
 
1.3%
3 5066
 
3.0%
4 39715
23.9%
5 46918
28.2%
6 72300
43.5%
ValueCountFrequency (%)
6 72300
43.5%
5 46918
28.2%
4 39715
23.9%
3 5066
 
3.0%
2 2207
 
1.3%
1 139
 
0.1%

Income
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.7318765
Minimum0
Maximum11
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-06-17T20:00:21.993431image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q15
median7
Q38
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.3560214
Coefficient of variation (CV)0.34997989
Kurtosis-0.27043377
Mean6.7318765
Median Absolute Deviation (MAD)2
Skewness-0.33390347
Sum1119814
Variance5.550837
MonotonicityNot monotonic
2024-06-17T20:00:22.090647image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
7 30365
18.3%
6 24323
14.6%
8 23928
14.4%
9 22301
13.4%
5 20561
12.4%
4 10016
 
6.0%
10 9156
 
5.5%
11 8929
 
5.4%
3 7063
 
4.2%
2 5652
 
3.4%
Other values (2) 4051
 
2.4%
ValueCountFrequency (%)
0 4
 
< 0.1%
1 4047
 
2.4%
2 5652
 
3.4%
3 7063
 
4.2%
4 10016
 
6.0%
5 20561
12.4%
6 24323
14.6%
7 30365
18.3%
8 23928
14.4%
9 22301
13.4%
ValueCountFrequency (%)
11 8929
 
5.4%
10 9156
 
5.5%
9 22301
13.4%
8 23928
14.4%
7 30365
18.3%
6 24323
14.6%
5 20561
12.4%
4 10016
 
6.0%
3 7063
 
4.2%
2 5652
 
3.4%

Race
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6491208
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-06-17T20:00:22.184887image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.7832138
Coefficient of variation (CV)1.0813118
Kurtosis6.9386434
Mean1.6491208
Median Absolute Deviation (MAD)0
Skewness2.8875997
Sum274323
Variance3.1798516
MonotonicityNot monotonic
2024-06-17T20:00:22.275159image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 137314
82.5%
2 10952
 
6.6%
8 8557
 
5.1%
7 2640
 
1.6%
4 2634
 
1.6%
3 2424
 
1.5%
6 1241
 
0.7%
5 583
 
0.4%
ValueCountFrequency (%)
1 137314
82.5%
2 10952
 
6.6%
3 2424
 
1.5%
4 2634
 
1.6%
5 583
 
0.4%
6 1241
 
0.7%
7 2640
 
1.6%
8 8557
 
5.1%
ValueCountFrequency (%)
8 8557
 
5.1%
7 2640
 
1.6%
6 1241
 
0.7%
5 583
 
0.4%
4 2634
 
1.6%
3 2424
 
1.5%
2 10952
 
6.6%
1 137314
82.5%

BMICat
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
3
59824 
4
56413 
2
42088 
0
6067 
1
 
1953

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters166345
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row4
4th row3
5th row2

Common Values

ValueCountFrequency (%)
3 59824
36.0%
4 56413
33.9%
2 42088
25.3%
0 6067
 
3.6%
1 1953
 
1.2%

Length

2024-06-17T20:00:22.389239image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-17T20:00:22.512742image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
3 59824
36.0%
4 56413
33.9%
2 42088
25.3%
0 6067
 
3.6%
1 1953
 
1.2%

Most occurring characters

ValueCountFrequency (%)
3 59824
36.0%
4 56413
33.9%
2 42088
25.3%
0 6067
 
3.6%
1 1953
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 166345
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 59824
36.0%
4 56413
33.9%
2 42088
25.3%
0 6067
 
3.6%
1 1953
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Common 166345
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 59824
36.0%
4 56413
33.9%
2 42088
25.3%
0 6067
 
3.6%
1 1953
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 166345
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 59824
36.0%
4 56413
33.9%
2 42088
25.3%
0 6067
 
3.6%
1 1953
 
1.2%

CurrSmoke
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
0
147007 
1
19338 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters166345
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 147007
88.4%
1 19338
 
11.6%

Length

2024-06-17T20:00:22.626327image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-17T20:00:22.743382image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 147007
88.4%
1 19338
 
11.6%

Most occurring characters

ValueCountFrequency (%)
0 147007
88.4%
1 19338
 
11.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 166345
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 147007
88.4%
1 19338
 
11.6%

Most occurring scripts

ValueCountFrequency (%)
Common 166345
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 147007
88.4%
1 19338
 
11.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 166345
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 147007
88.4%
1 19338
 
11.6%

CurrESmoke
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
0
163113 
1
 
3232

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters166345
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 163113
98.1%
1 3232
 
1.9%

Length

2024-06-17T20:00:22.839111image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-17T20:00:22.952694image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 163113
98.1%
1 3232
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 163113
98.1%
1 3232
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 166345
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 163113
98.1%
1 3232
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 166345
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 163113
98.1%
1 3232
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 166345
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 163113
98.1%
1 3232
 
1.9%

Alcohol30
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
1
84183 
0
82162 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters166345
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 84183
50.6%
0 82162
49.4%

Length

2024-06-17T20:00:23.043958image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-17T20:00:23.162502image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 84183
50.6%
0 82162
49.4%

Most occurring characters

ValueCountFrequency (%)
1 84183
50.6%
0 82162
49.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 166345
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 84183
50.6%
0 82162
49.4%

Most occurring scripts

ValueCountFrequency (%)
Common 166345
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 84183
50.6%
0 82162
49.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 166345
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 84183
50.6%
0 82162
49.4%

Residence
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
0
105678 
5
22923 
1
17829 
3
10831 
2
 
9084

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters166345
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row3
4th row2
5th row5

Common Values

ValueCountFrequency (%)
0 105678
63.5%
5 22923
 
13.8%
1 17829
 
10.7%
3 10831
 
6.5%
2 9084
 
5.5%

Length

2024-06-17T20:00:23.257734image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-17T20:00:23.380743image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 105678
63.5%
5 22923
 
13.8%
1 17829
 
10.7%
3 10831
 
6.5%
2 9084
 
5.5%

Most occurring characters

ValueCountFrequency (%)
0 105678
63.5%
5 22923
 
13.8%
1 17829
 
10.7%
3 10831
 
6.5%
2 9084
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 166345
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 105678
63.5%
5 22923
 
13.8%
1 17829
 
10.7%
3 10831
 
6.5%
2 9084
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Common 166345
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 105678
63.5%
5 22923
 
13.8%
1 17829
 
10.7%
3 10831
 
6.5%
2 9084
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 166345
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 105678
63.5%
5 22923
 
13.8%
1 17829
 
10.7%
3 10831
 
6.5%
2 9084
 
5.5%

FLUSHOT
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
0
103229 
1
62764 
9
 
352

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters166345
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 103229
62.1%
1 62764
37.7%
9 352
 
0.2%

Length

2024-06-17T20:00:23.489366image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-17T20:00:23.621301image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 103229
62.1%
1 62764
37.7%
9 352
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 103229
62.1%
1 62764
37.7%
9 352
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 166345
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 103229
62.1%
1 62764
37.7%
9 352
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 166345
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 103229
62.1%
1 62764
37.7%
9 352
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 166345
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 103229
62.1%
1 62764
37.7%
9 352
 
0.2%

EMPLOYED
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4825814
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-06-17T20:00:23.788950image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median7
Q37
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.8722685
Coefficient of variation (CV)0.64076216
Kurtosis-1.8302035
Mean4.4825814
Median Absolute Deviation (MAD)1
Skewness-0.21664695
Sum745655
Variance8.2499265
MonotonicityNot monotonic
2024-06-17T20:00:23.901541image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
7 76173
45.8%
1 54369
32.7%
2 14520
 
8.7%
8 10565
 
6.4%
5 4767
 
2.9%
3 3516
 
2.1%
4 2239
 
1.3%
6 196
 
0.1%
ValueCountFrequency (%)
1 54369
32.7%
2 14520
 
8.7%
3 3516
 
2.1%
4 2239
 
1.3%
5 4767
 
2.9%
6 196
 
0.1%
7 76173
45.8%
8 10565
 
6.4%
ValueCountFrequency (%)
8 10565
 
6.4%
7 76173
45.8%
6 196
 
0.1%
5 4767
 
2.9%
4 2239
 
1.3%
3 3516
 
2.1%
2 14520
 
8.7%
1 54369
32.7%

MARITAL
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9109561
Minimum0
Maximum6
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-06-17T20:00:24.018101image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q33
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3450059
Coefficient of variation (CV)0.70383921
Kurtosis1.1739034
Mean1.9109561
Median Absolute Deviation (MAD)0
Skewness1.4694317
Sum317878
Variance1.8090408
MonotonicityNot monotonic
2024-06-17T20:00:24.120773image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 96757
58.2%
2 26353
 
15.8%
3 24221
 
14.6%
5 12992
 
7.8%
6 3354
 
2.0%
4 2667
 
1.6%
0 1
 
< 0.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 96757
58.2%
2 26353
 
15.8%
3 24221
 
14.6%
4 2667
 
1.6%
5 12992
 
7.8%
6 3354
 
2.0%
ValueCountFrequency (%)
6 3354
 
2.0%
5 12992
 
7.8%
4 2667
 
1.6%
3 24221
 
14.6%
2 26353
 
15.8%
1 96757
58.2%
0 1
 
< 0.1%

HLTINSURE
Real number (ℝ)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9944453
Minimum1
Maximum88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-06-17T20:00:24.239813image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q33
95-th percentile10
Maximum88
Range87
Interquartile range (IQR)2

Descriptive statistics

Standard deviation13.606628
Coefficient of variation (CV)2.7243521
Kurtosis32.455386
Mean4.9944453
Median Absolute Deviation (MAD)1
Skewness5.8028312
Sum830801
Variance185.14032
MonotonicityNot monotonic
2024-06-17T20:00:24.337524image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
3 74485
44.8%
1 53816
32.4%
2 12795
 
7.7%
5 6619
 
4.0%
7 6039
 
3.6%
88 4258
 
2.6%
10 4224
 
2.5%
9 3464
 
2.1%
8 456
 
0.3%
4 165
 
0.1%
ValueCountFrequency (%)
1 53816
32.4%
2 12795
 
7.7%
3 74485
44.8%
4 165
 
0.1%
5 6619
 
4.0%
6 24
 
< 0.1%
7 6039
 
3.6%
8 456
 
0.3%
9 3464
 
2.1%
10 4224
 
2.5%
ValueCountFrequency (%)
88 4258
 
2.6%
10 4224
 
2.5%
9 3464
 
2.1%
8 456
 
0.3%
7 6039
 
3.6%
6 24
 
< 0.1%
5 6619
 
4.0%
4 165
 
0.1%
3 74485
44.8%
2 12795
 
7.7%

KIDNEYDIS
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
0
157336 
1
 
9009

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters166345
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 157336
94.6%
1 9009
 
5.4%

Length

2024-06-17T20:00:24.453589image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-17T20:00:24.574613image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 157336
94.6%
1 9009
 
5.4%

Most occurring characters

ValueCountFrequency (%)
0 157336
94.6%
1 9009
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 166345
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 157336
94.6%
1 9009
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
Common 166345
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 157336
94.6%
1 9009
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 166345
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 157336
94.6%
1 9009
 
5.4%

DEPRESSDIS
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
0
136041 
1
30304 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters166345
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 136041
81.8%
1 30304
 
18.2%

Length

2024-06-17T20:00:24.680757image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-17T20:00:24.802772image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 136041
81.8%
1 30304
 
18.2%

Most occurring characters

ValueCountFrequency (%)
0 136041
81.8%
1 30304
 
18.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 166345
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 136041
81.8%
1 30304
 
18.2%

Most occurring scripts

ValueCountFrequency (%)
Common 166345
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 136041
81.8%
1 30304
 
18.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 166345
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 136041
81.8%
1 30304
 
18.2%

RENT
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
1
139707 
0
23284 
3
 
3354

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters166345
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 139707
84.0%
0 23284
 
14.0%
3 3354
 
2.0%

Length

2024-06-17T20:00:24.905445image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-17T20:00:25.046804image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 139707
84.0%
0 23284
 
14.0%
3 3354
 
2.0%

Most occurring characters

ValueCountFrequency (%)
1 139707
84.0%
0 23284
 
14.0%
3 3354
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 166345
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 139707
84.0%
0 23284
 
14.0%
3 3354
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Common 166345
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 139707
84.0%
0 23284
 
14.0%
3 3354
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 166345
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 139707
84.0%
0 23284
 
14.0%
3 3354
 
2.0%

BLIND
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
0
157198 
1
 
9147

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters166345
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 157198
94.5%
1 9147
 
5.5%

Length

2024-06-17T20:00:25.169812image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-17T20:00:25.311172image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 157198
94.5%
1 9147
 
5.5%

Most occurring characters

ValueCountFrequency (%)
0 157198
94.5%
1 9147
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 166345
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 157198
94.5%
1 9147
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Common 166345
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 157198
94.5%
1 9147
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 166345
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 157198
94.5%
1 9147
 
5.5%

DECISION
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
0
151653 
1
 
14692

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters166345
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 151653
91.2%
1 14692
 
8.8%

Length

2024-06-17T20:00:25.419300image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-17T20:00:25.535364image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 151653
91.2%
1 14692
 
8.8%

Most occurring characters

ValueCountFrequency (%)
0 151653
91.2%
1 14692
 
8.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 166345
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 151653
91.2%
1 14692
 
8.8%

Most occurring scripts

ValueCountFrequency (%)
Common 166345
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 151653
91.2%
1 14692
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 166345
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 151653
91.2%
1 14692
 
8.8%

Interactions

2024-06-17T20:00:14.731502image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:04.118104image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:05.535176image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:06.867431image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:08.222501image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:09.517061image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:10.783347image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:12.095761image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:13.488528image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:14.864430image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:04.260457image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:05.691912image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:07.014246image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:08.352948image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:09.663875image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:11.003570image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:12.226705image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:13.625920image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:14.994382image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:04.399833image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:05.848151image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:07.170486image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:08.492325image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:09.818627image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:11.144434image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:12.363601image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:13.759839image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:15.129294image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:04.619560image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:05.988023image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:07.319782image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:08.634676image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:09.958499image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:11.279842image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:12.509921image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:13.911615image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:15.257758image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:04.751992image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:06.141783image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:07.469573image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:08.769588image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:10.097875image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:11.412273image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:12.668641image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:14.051487image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:15.383742image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:04.886904image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:06.290583image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:07.622838image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:08.909461image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:10.227827image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:11.542721image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:12.859601image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:14.188383image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:15.520142image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:05.042152image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:06.439879image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:07.780565image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:09.062724image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:10.369186image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:11.685570image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:13.073376image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:14.329743image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:15.661998image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:05.237576image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:06.588183image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:07.930357image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:09.222435image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:10.513026image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:11.828418image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:13.224161image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:14.468127image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:15.794925image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:05.392824image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:06.727063image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:08.093541image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:09.371732image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:10.652899image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:11.962337image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:13.359072image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-06-17T20:00:14.606014image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2024-06-17T20:00:25.695572image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
GenHlthMentHlthPhysHlthEducationIncomeRaceEMPLOYEDMARITALHLTINSUREDiabetesHighBPHighCholCholCheckSmokerStrokeHeartDiseaseorAttackPhysActivityFruitsVeggiesHvyAlcoholConsumpAnyHealthcareNoDocbcCostDiffWalkSexAgeBMICatCurrSmokeCurrESmokeAlcohol30ResidenceFLUSHOTKIDNEYDISDEPRESSDISRENTBLINDDECISION
GenHlth1.0000.2320.465-0.243-0.3420.1090.2500.1430.1890.2790.2710.1520.0470.1590.1760.2540.3100.0930.1160.0360.0340.1510.4660.0160.0490.1190.1430.0450.2040.0260.0450.1960.2160.1300.1940.295
MentHlth0.2321.0000.305-0.024-0.1220.0160.0460.0920.0310.0650.0470.0550.0120.0890.0650.0620.1450.0600.0590.0260.0400.1610.2190.1170.0690.0370.1300.0620.0650.0160.0610.0550.4110.0930.1230.360
PhysHlth0.4650.3051.000-0.107-0.2160.0290.2030.0840.1330.1520.1230.0810.0290.1080.1300.1620.2590.0430.0680.0240.0090.1330.4390.0500.0180.0560.1030.0410.1470.0090.0100.1420.2150.0940.1540.268
Education-0.243-0.024-0.1071.0000.432-0.103-0.137-0.116-0.1670.1010.1000.0330.0520.1810.0650.0770.2180.1160.1390.0130.1280.1040.1770.0460.0400.0520.1860.0440.1910.0460.0410.0370.0440.1370.1180.136
Income-0.342-0.122-0.2160.4321.000-0.152-0.384-0.415-0.4200.1450.1460.0640.0560.1500.1200.1170.2420.0610.1470.0520.1220.1800.3180.1270.1470.0460.1940.0370.2780.0710.1030.0900.1550.2590.1890.239
Race0.1090.0160.029-0.103-0.1521.000-0.0240.0970.0580.0960.0930.0140.0300.0680.0390.0320.0610.0220.1060.0380.1040.1060.0780.0420.0700.0500.0670.0130.1010.0780.0800.0220.0460.1370.0870.060
EMPLOYED0.2500.0460.203-0.137-0.384-0.0241.0000.1330.4590.1510.1790.1130.0890.1340.1430.1600.1810.0730.0770.0430.1700.1790.3600.1790.3660.0590.1740.0660.1800.1320.3460.1250.2240.1650.1700.295
MARITAL0.1430.0920.084-0.116-0.4150.0970.1331.0000.1850.0550.0940.0200.0650.1130.0670.0640.1160.0490.1160.0360.0900.1000.1680.1760.2160.0350.1500.0480.1310.0950.1340.0540.1200.2320.0870.116
HLTINSURE0.1890.0310.133-0.167-0.4200.0580.4590.1851.0000.0190.0450.0140.1900.0290.0120.0200.0280.0230.0310.0231.0000.2430.0080.0240.0940.0100.0930.0260.0190.0490.0880.0220.0040.0800.0270.028
Diabetes0.2790.0650.1520.1010.1450.0960.1510.0550.0191.0000.2290.1450.0760.0380.0900.1540.1420.0380.0510.0630.0190.0330.1870.0490.0770.2010.0000.0000.1470.0240.0660.1560.0680.0720.0820.084
HighBP0.2710.0470.1230.1000.1460.0930.1790.0940.0450.2291.0000.2040.1240.0600.1090.1700.1180.0420.0400.0000.0440.0110.1710.0540.1830.2190.0070.0070.0790.0730.1470.1270.0550.0390.0630.062
HighChol0.1520.0550.0810.0330.0640.0140.1130.0200.0140.1450.2041.0000.0480.0650.0620.1330.0540.0360.0350.0000.0130.0190.0760.0310.1080.0930.0120.0120.0190.0320.0980.0650.0880.0140.0400.063
CholCheck0.0470.0120.0290.0520.0560.0300.0890.0650.1900.0760.1240.0481.0000.0260.0240.0490.0140.0210.0160.0340.1890.0750.0310.0320.0800.0520.0840.0200.0030.0330.1000.0310.0290.0430.0060.000
Smoker0.1590.0890.1080.1810.1500.0680.1340.1130.0290.0380.0600.0650.0261.0000.0500.1060.0920.0870.0330.0920.0280.0480.1110.0800.0620.0320.4060.1220.0290.0090.0330.0260.0860.0870.0490.083
Stroke0.1760.0650.1300.0650.1200.0390.1430.0670.0120.0900.1090.0620.0240.0501.0000.1600.0750.0060.0290.0140.0120.0350.1500.0100.0880.0230.0420.0090.0690.0290.0450.0770.0580.0600.0900.112
HeartDiseaseorAttack0.2540.0620.1620.0770.1170.0320.1600.0640.0200.1540.1700.1330.0490.1060.1601.0000.0890.0160.0260.0290.0190.0300.1650.1090.1590.0560.0370.0070.0750.0400.1060.1300.0510.0420.0820.082
PhysActivity0.3100.1450.2590.2180.2420.0610.1810.1160.0280.1420.1180.0540.0140.0920.0750.0891.0000.1280.1370.0130.0280.0650.2970.0560.0820.1550.1110.0260.1570.0460.0090.0810.1100.1050.0940.133
Fruits0.0930.0600.0430.1160.0610.0220.0730.0490.0230.0380.0420.0360.0210.0870.0060.0160.1281.0000.2110.0400.0230.0330.0460.0480.0880.0920.1080.0410.0060.0510.0740.0120.0510.0400.0180.048
Veggies0.1160.0590.0680.1390.1470.1060.0770.1160.0310.0510.0400.0350.0160.0330.0290.0260.1370.2111.0000.0140.0300.0370.0790.0390.0050.0470.0560.0130.0760.0130.0240.0260.0470.0890.0430.065
HvyAlcoholConsump0.0360.0260.0240.0130.0520.0380.0430.0360.0230.0630.0000.0000.0340.0920.0140.0290.0130.0400.0141.0000.0180.0060.0320.0000.0570.0420.0760.0280.2410.0300.0330.0290.0170.0110.0090.000
AnyHealthcare0.0340.0400.0090.1280.1220.1040.1700.0901.0000.0190.0440.0130.1890.0280.0120.0190.0280.0230.0300.0181.0000.2410.0050.0240.1260.0140.0910.0250.0190.0680.1140.0220.0020.1100.0260.023
NoDocbcCost0.1510.1610.1330.1040.1800.1060.1790.1000.2430.0330.0110.0190.0750.0480.0350.0300.0650.0330.0370.0060.2411.0000.1110.0230.1160.0360.1060.0400.0470.0680.1070.0200.1030.1290.0950.140
DiffWalk0.4660.2190.4390.1770.3180.0780.3600.1680.0080.1870.1710.0760.0310.1110.1500.1650.2970.0460.0790.0320.0050.1111.0000.0730.1310.1870.0970.0320.1710.0510.0620.1500.1890.1600.1830.264
Sex0.0160.1170.0500.0460.1270.0420.1790.1760.0240.0490.0540.0310.0320.0800.0100.1090.0560.0480.0390.0000.0240.0230.0731.0000.0190.1870.0000.0000.1080.1050.0080.0000.1330.0240.0170.036
Age0.0490.0690.0180.0400.1470.0700.3660.2160.0940.0770.1830.1080.0800.0620.0880.1590.0820.0880.0050.0570.1260.1160.1310.0191.0000.0770.1230.0640.1010.1880.4310.0920.0910.0630.0590.047
BMICat0.1190.0370.0560.0520.0460.0500.0590.0350.0100.2010.2190.0930.0520.0320.0230.0560.1550.0920.0470.0420.0140.0360.1870.1870.0771.0000.0830.0080.0890.0290.0430.0550.0900.0420.0310.055
CurrSmoke0.1430.1300.1030.1860.1940.0670.1740.1500.0930.0000.0070.0120.0840.4060.0420.0370.1110.1080.0560.0760.0910.1060.0970.0000.1230.0831.0000.1270.0230.0480.1150.0070.0960.1510.0620.106
CurrESmoke0.0450.0620.0410.0440.0370.0130.0660.0480.0260.0000.0070.0120.0200.1220.0090.0070.0260.0410.0130.0280.0250.0400.0320.0000.0640.0080.1271.0000.0000.0340.0500.0000.0660.0440.0210.063
Alcohol300.2040.0650.1470.1910.2780.1010.1800.1310.0190.1470.0790.0190.0030.0290.0690.0750.1570.0060.0760.2410.0190.0470.1710.1080.1010.0890.0230.0001.0000.0690.0340.0770.0560.1080.0780.089
Residence0.0260.0160.0090.0460.0710.0780.1320.0950.0490.0240.0730.0320.0330.0090.0290.0400.0460.0510.0130.0300.0680.0680.0510.1050.1880.0290.0480.0340.0691.0000.1620.0290.0330.0850.0100.025
FLUSHOT0.0450.0610.0100.0410.1030.0800.3460.1340.0880.0660.1470.0980.1000.0330.0450.1060.0090.0740.0240.0330.1140.1070.0620.0080.4310.0430.1150.0500.0340.1621.0000.0750.0390.0580.0000.041
KIDNEYDIS0.1960.0550.1420.0370.0900.0220.1250.0540.0220.1560.1270.0650.0310.0260.0770.1300.0810.0120.0260.0290.0220.0200.1500.0000.0920.0550.0070.0000.0770.0290.0751.0000.0580.0410.0720.063
DEPRESSDIS0.2160.4110.2150.0440.1550.0460.2240.1200.0040.0680.0550.0880.0290.0860.0580.0510.1100.0510.0470.0170.0020.1030.1890.1330.0910.0900.0960.0660.0560.0330.0390.0581.0000.1180.0870.310
RENT0.1300.0930.0940.1370.2590.1370.1650.2320.0800.0720.0390.0140.0430.0870.0600.0420.1050.0400.0890.0110.1100.1290.1600.0240.0630.0420.1510.0440.1080.0850.0580.0410.1181.0000.1040.140
BLIND0.1940.1230.1540.1180.1890.0870.1700.0870.0270.0820.0630.0400.0060.0490.0900.0820.0940.0180.0430.0090.0260.0950.1830.0170.0590.0310.0620.0210.0780.0100.0000.0720.0870.1041.0000.174
DECISION0.2950.3600.2680.1360.2390.0600.2950.1160.0280.0840.0620.0630.0000.0830.1120.0820.1330.0480.0650.0000.0230.1400.2640.0360.0470.0550.1060.0630.0890.0250.0410.0630.3100.1400.1741.000

Missing values

2024-06-17T20:00:16.014157image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-17T20:00:16.825613image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DiabetesHighBPHighCholCholCheckSmokerStrokeHeartDiseaseorAttackPhysActivityFruitsVeggiesHvyAlcoholConsumpAnyHealthcareNoDocbcCostGenHlthMentHlthPhysHlthDiffWalkSexAgeEducationIncomeRaceBMICatCurrSmokeCurrESmokeAlcohol30ResidenceFLUSHOTEMPLOYEDMARITALHLTINSUREKIDNEYDISDEPRESSDISRENTBLINDDECISION
000011000110105102000545110001171300100
1111100101001020000543230001073200100
21101000111010210000447140013071200100
31001011111010503011534730002181300100
4001110000001030011656120005171300100
50111100111010352510448140011071300100
60101100001010425000453131002172301100
70001100101010200004681000010711000100
8101100100101040001567120003171300101
9010100001101020000346130011015101100
DiabetesHighBPHighCholCholCheckSmokerStrokeHeartDiseaseorAttackPhysActivityFruitsVeggiesHvyAlcoholConsumpAnyHealthcareNoDocbcCostGenHlthMentHlthPhysHlthDiffWalkSexAgeEducationIncomeRaceBMICatCurrSmokeCurrESmokeAlcohol30ResidenceFLUSHOTEMPLOYEDMARITALHLTINSUREKIDNEYDISDEPRESSDISRENTBLINDDECISION
1663350011100111010115010568120010012301101
1663360011000110000100013542200000758800100
166337010100010001030001466220010071100100
166338000100010101030001368240010012100100
166339110100000101020011445240010111300100
166340001100011101020001441220000071300300
1663410100000101010300013452200000451000101
166342110100011101040001423820010074300310
16634300010001110102000136102400100711000100
166344011100011101020001446220010072300100

Duplicate rows

Most frequently occurring

DiabetesHighBPHighCholCholCheckSmokerStrokeHeartDiseaseorAttackPhysActivityFruitsVeggiesHvyAlcoholConsumpAnyHealthcareNoDocbcCostGenHlthMentHlthPhysHlthDiffWalkSexAgeEducationIncomeRaceBMICatCurrSmokeCurrESmokeAlcohol30ResidenceFLUSHOTEMPLOYEDMARITALHLTINSUREKIDNEYDISDEPRESSDISRENTBLINDDECISION# duplicates
639001100011101010000361112001001110010032
782001100011101010001361113001001110010023
104100110001110102000136913001001110010023
60800110001110101000036912001001110010019
777001100011101010001361112001001110010019
950001100011101020000361112001001110010018
93000110001110102000036912001001110010017
471001100010101010001361113001001110010015
632001100011101010000361012001001110010015
76200110001110101000136913001001110010015